Shadows in images have influence on machine vision algorithms for edge extraction and image matching. A color image is composed of three grayscale component images of RGB. Since the grayscale images of the three components are quite similar, the machine vision algorithms usually convert them into one grayscale component for processing. Since all of the three grayscale components contain brightness information, the interference problem of illumination changes exists by directly using the three components or using a grayscale component composed of the weighted sum and the maximum and minimum values thereof. Especially in the presence of shadows, the illumination changes have a great influence on the machine vision algorithms. The pre-processing of shadows by the existing methods is mainly divided into three types: anti-shadow feature extractor, shadow-free feature extractor, and shadow detection and removal method.
1. Anti-shadow feature extractor Brightness components and color components insensitive to illumination changes are extracted through color space conversion, and color components are used for subsequent processing. Since the description of color space is based on an ideal model, although the extractors can reduce shadow interference to some extent, shadows in an image cannot be completely removed. Especially in the case of strong shadows, the extractors may fail completely.
2. Shadow-free feature extractor A shadow-free feature extractor obtained through theoretical analysis and experiments of imaging physics can completely remove shadows. However, the existing methods are still unable to deal with certain strong shadow situations.
3. Shadow detection and removal method Firstly, a shadow region is detected, and then the part with the shadow is repaired by using a restoration algorithm. This method is often complicated and slow in processing, and most of the existing methods may cause missing of the detailed information of the shadow region to varying degrees.
On the whole, the existing shadow processing methods cannot realize a good shadow removal effect while ensuring real-time performance (fast processing speed), especially in the case of strong shadows. This makes shadow interference a problem for many machine vision tasks requiring high real-time performance, such as road detection in the intelligent vehicle perception technology.
The intelligent vehicle perception technology enables vehicles to actively perceive the surrounding environment, thus actively preventing traffic accidents and even realizing automatic driving. Road identification is a prerequisite for driving, so road detection is an indispensable part of intelligent vehicle perception. A road detection method processes a road image from the view of a driver and detects the region where the road surface is located. Trees and buildings on the roadside may leave shadows on the road, especially when the light is strong, the interference of shadows is very serious, which makes it difficult to correctly detect the road region.